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I had a general question on what to do when no ground truth data is available and clustering is initiated.

Are there still metrics which can indicate how good or bad the clustering worked on the "baseline" data set? I am not sure how to tune the initial model without appropriate measures for (internal) model performance on the baseline data.

What is a best practice?

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This article lists a number of internal cluster validity indices: https://www.ncbi.nlm.nih.gov/pubmed/26389570.

These internal indices are used to determine the quality of your clusters and some of these have been implemented in sklearn. Considering your tag I am going to assume that you are using Python.

Here is a link to the cluster performance metrics implemented in sklearn, there is also an example: https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics.

Hope this answered your question.

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